1 TL;DR

Just a bit of dataknut fun woven around the day job.

You’ll be wanting Section 6 for the trending hashtags…

2 Terms of re-use

2.1 License

CC-BY-SA unless otherwise noted.

2.2 Citation

3 Purpose

The idea is to extract and visualise tweets and re-tweets of #edsclimatechat OR #ccbc18 (see www.climateandbusiness.com).

Why? Err…. Just. Because.

4 How it works

Code borrows extensively from https://github.com/mkearney/rtweet

The analysis used rtweet to ask the Twitter search API to extract ‘all’ tweets containing the #edsclimatechat OR #ccbc18 hashtags in the ‘recent’ twitterVerse.

It is therefore possible that not quite all tweets have been extracted although it seems likely that we have captured most recent human tweeting which was the main intention. Future work should instead use the Twitter streaming API.

## [1] "Found 7 files matching #edsclimatechat OR #ccbc18 in ~/Data/twitter/"

The data has:

5 Analysis

5.1 Tweets and Tweeters over time

Number of tweets and tweeters

Figure 5.1: Number of tweets and tweeters

Figure 5.1 shows the number of tweets and tweeters in the data extract by day. The quotes, tweets and re-tweets have been separated.

If you are in New Zealand and you are wondering why there are no tweets today (2018-10-09) the answer is that twitter data (and these plots) are working in UTC and (y)our today() may not have started yet in UTC. Don’t worry, all the tweets are here - it’s just our old friend the timezone… :-)

5.2 Who’s tweeting?

Next we’ll try by screen name.

N tweets per day by screen name

Figure 5.2: N tweets per day by screen name

Figure 5.2 is a really bad visualisation of all tweeters tweeting over time. Each row of pixels is a tweeter (the names are probably illegible) and a green dot indicates a few tweets in the given day while a red dot indicates a lot of tweets.

So let’s re-do that for the top 50 tweeters so we can see their tweetStreaks (tm)…

Top tweeters:

Table 5.1: Top 15 tweeters (all days)
screen_name nTweets
AlecTang_ 45
GoodSenseMktg 42
Davidxvx 30
abbie_reynolds 21
anarkaytie 19
aidselva 18
DanHikuroa 16
KatevP 14
gauloir 12
ENVIR_HEALTH 11
TheOuterGarden 11
SilverLiningGS 11
MaryStGeorge 11
E_MSolutions 10
CaptainKettles 9

And their tweetStreaks are shown in Figure 5.3

N tweets per day minutes by screen name (top 50, reverse alphabetical)

Figure 5.3: N tweets per day minutes by screen name (top 50, reverse alphabetical)

Any twitterBots…?

5.3 Which hashtags are mentioned the most?

This is very quick and dirty but… to calculate this we have to do a bit of string processing first.

This is how I have tidied the hashtags (make other suggestions here):

# First we make everything lower case
htLongDT <- htLongDT[, `:=`(htLower, tolower(htOrig))]  # lower case

# Next we remove the macrons just in case h/t:
# https://twitter.com/Thoughtfulnz/status/1046685305569345536
htLongDT <- htLongDT[, `:=`(htClean, stringr::str_replace_all(htLower, "[āēīōū]", 
    hashTagR::deMacron))]

# we might need to do other things here depending on the the context

Table 5.2 shows the total count of each #hashtag by (re)tweet type.

Table 5.2: Top 20 hashtags
hashTag type count
ccbc18 Tweet 195
edsclimatechat Tweet 182
ccbc18 Re-tweet 62
edsclimatechat Re-tweet 46
climatechange Tweet 34
climateaction Re-tweet 22
climateaction Tweet 22
climate Re-tweet 21
climatechange Re-tweet 16
climate Tweet 14
ccbc2018 Tweet 13
ccbc18 Quote 12
edsclimatechat Quote 11
letsdothis Re-tweet 10
nz Tweet 9
climateleaderscoalition Tweet 8
zerocarbonbill Re-tweet 8
sr15 Tweet 6
zerocarbonact Tweet 6
climateleaderscoalition Re-tweet 5

Figure 5.4 plots the daily occurence of these hashtags after removing variants of #edsclimatechat OR #ccbc18 and selecting only those which have more than 10 mentions on any day. For clarity tweets and re-tweets are aggregated. See Section 7 for the problems with this #hashTag counting approach.

Most mentioned #hashtags per day

Figure 5.4: Most mentioned #hashtags per day

6 Most popular hashtags over time

There are a lot of problems with this approach (see Section 7) but Figure 6.1 shows trends over time (watch for lines of apparently dis-similar hashtags where the macron fix has failed) and Figure 6.2 shows the totals to date.

Figure 6.1 uses plotly to avoid having to render a large legend - just hover over the lines to see who is who…

Figure 6.1: Cumulative hashtag counts over time (only total count > 30 shown)

Total hashtag counts to date (only total count > 30 shown)

Figure 6.2: Total hashtag counts to date (only total count > 30 shown)

7 Problems

Loads of them. But primarily:

8 About

As ever, #YMMV.

Analysis completed in 4.917 seconds ( 0.08 minutes) using knitr in RStudio with R version 3.5.1 (2018-07-02) running on x86_64-apple-darwin15.6.0.

A special mention must go to https://github.com/mkearney/rtweet (Kearney 2018) for the twitter API interaction functions.

Other R packages used:

References

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